Development of microscale wind maps for Phaluay Island, Thailand

Promsen, Worrapass; Masiri, Itsara; Janjai, Serm

doi:10.1016/j.proeng.2012.01.1281

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…The most widely used CFD-based software products for wind farm design are Meteodyn WT and WindSim. A study used mesoscale wind data and Meteodyn WT to evaluate the wind conditions on Phaluay Island in Thailand with a spatial resolution of 90 m × 90 m. The result accurately conformed to the mesoscale wind data [24]. Another study used Meteodyn WT to evaluate a wind farm on complex terrain.…”

Section: Introductionmentioning

confidence: 89%

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Yue

Liu

et al. 2019

Energies

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In this study we evaluated the wind resources of wind farms in the Changhua offshore area of Taiwan. The offshore wind farm in Zone of Potential (ZoP) 26 was optimized through an economic evaluation. The annual energy production (AEP) of the offshore wind farm in ZoP 26 was predicted for 10 and 25 years with probabilities of 50%, 75%, and 90% by using measured mast data, measure-correlate-predict (MCP) data derived from Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Central Weather Bureau (CWB) data. When the distance between the turbines in a wind farm was decreased from 12D to 6D, the turbine number increased from 53 to 132, while the capacity factor decreased slightly from 48.6% to 47.6%. MCP data derived from the inland CWB station with similar levels of wind resources can be used to accurately predict the power generation of the target offshore wind farm. The use of MCP with mast data as target data, together with CWB and MERRA data as reference data, proved to be a feasible method for predicting offshore wind power generation in places where a mast is available in a neighboring area.

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Section: Introductionmentioning

confidence: 89%

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Yue

Liu

et al. 2019

Energies

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“…In this paper, in order to obtain the orographic map shown in Figure 1 and the annual average wind speed map shown in Figure 2, the Meteodyn WT wind resource assessment software was used [28,29,30]. Here, the input variables used were the following: topographic and roughness data of the VWF; wind speed data of the meteorological tower located in the VWF; guaranteed power curve of the WTs; air density of the VWF (0.923 kg/m 3 ); hub height of the WTs; wind turbine positions in UTM coordinates; and meteorological tower position in UTM coordinates.…”

Section: Data and Methods Used For The Estimation Of The Power Curvementioning

confidence: 99%

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Hernández

Méndez

Maldonado

et al. 2016

Sensors

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Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets.

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“…Such presentations of information are ideal to determine hot spots of resource availability and to select the best locations for installations that generate as much energy as possible. When these resource availability maps are integrated in GIS, it is possible to combine the maps with multiple technical, economical and regulatory parameters in order to estimate deployment potentials of PV installations [57][58][59][60][61] or wind parks [62][63][64][65][66][67][68][69].…”

Section: Gis and Biomassmentioning

confidence: 99%

“…Nowadays, such high-resolution atlases can be found for locations all around the globe and are generated also with alternative methodologies such as atmospheric mesoscale models. Examples include the 3 km × 3 km wind atlas of Thailand [67] and the British Islands [116], the 100 m × 100 m Austrian [117] and Bavarian [118] wind maps and the microscale 90 m × 90 m wind map of the Phaluay Island (Thailand) [68]. In all these cases, reanalysis data such as the ERA-40 [119] or the ERA-INTERIM [120] of the European Centre for Medium-Range Weather Forecast (ECMWF) or the Climate Forecast System Reanalysis (CFSR) of the National Centres for Environmental Prediction (NCEP) [121] are brought to a higher spatial resolution using atmospheric models such as the Karlsruhe Atmospheric Mesoscale Model (KAMM) or the Weather Research and Forecasting model (WRF).…”

Section: Gis and Biomassmentioning

confidence: 99%

Spatiotemporal modelling for integrated spatial and energy planning

Camargo

Stoeglehner

2018

Energ Sustain Soc

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The transformation of the energy system to a renewable one is crucial to enable sustainable development for mankind. The integration of high shares of renewable energy sources (RES) in the energy matrix is, however, a major challenge due to the low energy density per area unit and the stochastic temporal patterns in which RES are available. Distributed generation for energy supply becomes necessary to overcome this challenge, but it sets new pressures on the use of space. To optimize the use of space, spatial planning and energy planning have to be integrated, and suitable tools to support this integrated planning process are fundamental. Spatiotemporal modelling of RES is an emerging research field that aims at supporting and improving the planning process of energy systems with high shares of RES. This paper contributes to this field by reviewing latest developments and proposing models and tools for planning distributed energy systems for municipalities. The models provide estimations of the potentials of fluctuating RES technologies and energy demand in high spatiotemporal resolutions, and the planning tools serve to configure energy systems of multiple technologies that are customized for the local energy demand. Case studies that test the spatiotemporal models and their transferability were evaluated to determine the advantages of using these instead of merely spatial models for planning municipality-wide RES-based energy systems. Spatiotemporal models allow a more detailed estimation of RES potentials and serve to find not only optimal locations but also optimal sizes for individual RES plants. While the potential of variable RES based on yearly energy generation values can be considerably larger than the energy demand, only a fraction of it can be deployed without compromising the quality and reliability of the local energy supply system. Furthermore, when spatiotemporal models are used, it can be seen that technological diversity is beneficial for the supply quality. Similarly, the advantages and limits of the deployment of storage systems and of combinations of RES-based technologies to cover the local demand were determined and evaluated. Finally, the results from the analyses provide sufficient information to define road maps of installations deployment to achieve desired RES penetration objectives.

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Development of microscale wind maps for Phaluay Island, Thailand

Cited by 11 publications

References 5 publications

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Spatiotemporal modelling for integrated spatial and energy planning

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